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title: Call for Papers
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<h1>{{ site.conference.short_name }} {{ site.conference.year }} Call for Papers</h1>

<p>We invite submissions to the 2021 International Conference on Artificial Intelligence and Statistics
 (AISTATS), and welcome paper submissions on artificial intelligence, machine learning, statistics,
 and related areas.
</p>

<h3>Key dates:</h3>

<p>The tentative dates are as follow:</p>
<ul>
<li><p>Abstract submission: October 8, 2020, 08:00 AM PDT </p></li>
<li><p>Submission date: October 15, 2020, 08:00 AM PDT</p></li>
<li><p>Reviews released: November 23, 2020</p></li>
<li><p>Author rebuttals due: November 28, 2020</p></li>
<li><p>Final decisions: January 08, 2021</p></li>
<li><p>Conference dates: April 13-15, 2021</p></li>
</ul>


<h3>Summary</h3>
<p>AISTATS is an interdisciplinary gathering of researchers at the
intersection of computer science, artificial intelligence, machine
learning, statistics, and related areas. Since its inception in
1985, the primary goal of AISTATS has been to broaden research
in these fields by promoting the exchange of ideas among them.
We encourage the submission of all papers which are in keeping
with this objective at AISTATS.
</p>

<p>Current website: <a href="https://www.aistats.org/aistats2021/">
    https://www.aistats.org/aistats2021/</a></p>
</p>



<h1>Paper Submission:</h1>

<p><b>Proceedings track:</b> This is the standard AISTATS paper submission
track. Papers will be selected via a rigorous double-blind peer-review
process. All accepted papers will be presented at the Conference as contributed
talks or as posters and will be published in the Proceedings.</p>

<p>Solicited topics include, but are not limited to:</p>

<ul>
<li><p>Models and estimation: graphical models, causality, Gaussian processes,
	approximate inference, kernel methods, nonparametric models, statistical and
	computational learning theory, manifolds and embedding, sparsity and
	compressed sensing, ... </p></li>
<li><p>Classification, regression, density estimation, unsupervised and
	semi-supervised learning, clustering, topic models, ... </p></li>
<li><p>Structured prediction, relational learning, logic and probability </p></li>
<li><p>Reinforcement learning, planning, control </p></li>
<li><p>Game theory, no-regret learning, multi-agent systems </p></li>
<li><p>Algorithms and architectures for high-performance computation in
	AI and statistics </p></li>
<li><p>Software for and applications of AI and statistics </p></li>
<li><p>Deep learning including optimization, generalization and architectures </p></li>
<li><p>Trustworthy learning, including learning with privacy and fairness,
	interpretability, and robustness </p></li>
</ul>



<h1>Formatting and Supplementary Material</h1>
<p>Submissions are limited to 8 pages <i>excluding</i> references using the LaTeX
	style file we provide below. The number of pages containing citations alone
	is not limited. You can also submit a single file of additional supplementary
	material which may be either a pdf file (such as proof details) or a zip file
	for other formats/more files (such as code or videos). Note that reviewers are
	under no obligation to examine your supplementary material. If you have only
	one supplementary pdf file, please upload it as is; otherwise gather
	everything to the single zip file.</p>

<p>Submissions will be through CMT (<a href="https://cmt3.research.microsoft.com/AISTATS2021/">
    https://cmt3.research.microsoft.com/AISTATS2021/</a>) and will be open a
month before the abstract submission deadline.</p>
</p>

<p>Formatting information (including LaTeX style files) will be made
available. We do not support submission in preparation systems other than
LaTeX. Please do not modify the layout given by the style file. If you have
questions about the style file or its usage, please contact the publications
chair.</p>






<h1>Anonymization Requirements</h1>

<p>The AISTATS review process is double-blind. Please remove all identifying
	information from your submission, including author names, affiliations,
	and any acknowledgments. Self-citations can present a special problem: we
	recommend leaving in a moderate number of self-citations for published or
	otherwise well-known work. For unpublished or less-well-known work, or
	for large numbers of self-citations, it is up to the author's discretion
	how best to preserve anonymity. Possibilities include leaving out a
	citation altogether, including it but replacing the citation text with
	"removed for anonymous submission," or leaving the citation as-is; authors
	should choose for each citation the treatment which is least likely to
	reveal authorship.</p>

<p>Previous tech-report or workshop versions of a paper can similarly present a
	problem for anonymization. We suggest <i>leaving out</i> any identifying
	information for such versions, but bringing them to the attention of the
	program committee via the submission page. Reviewers will be instructed
	that tech reports (including reports on sites such as <a href="http://arxiv.org/">arXiv</a>) and papers
	in workshops without archival proceedings do not count as prior publication.</p>




<h1>Previous or Concurrent Submissions</h1>

<p>Submitted manuscripts should not have been previously published in a
	journal or in the proceedings of a conference, and should not be under
	consideration for publication at another conference at any point during
	the AISTATS review process. It is acceptable to have a substantially
	extended version of the submitted paper under consideration simultaneously
	for journal publication, so long as the journal version's planned
	publication date is in May 2021 or later, the journal submission does
	not interfere with AISTATS's right to publish the paper, and the situation
	is clearly described at the time of AISTATS submission. Please describe
	the situation in the appropriate box on the submission page (and do not
	include author information in the submission itself, to avoid accidental
	unblinding). </p>

<p>As mentioned above, reviewers will be instructed that tech reports
	(including reports on sites such as arXiv) and papers in workshops without
	archival proceedings do not count as prior publication.</p>

<p>All accepted papers will be presented at the Conference either as
	contributed talks or as posters, and will be published in the AISTATS
	Conference Proceedings in the Journal of Machine Learning Research
	Workshop and Conference Proceedings series. Papers for talks and posters
	will be treated equally in publication.</p>


<br>
<p>Arindam Banerjee and Kenji Fukumizu<br>AISTATS 2021 Program Chairs</p>
<p> </p>
<br>
<br>




